Probabilistic Boolean Networks (PBNs) were introduced in [1]} for modelling Gene Regulatory Networks (GRNs) as complex dynamical systems. Nodes represent genes in one of two possible states of activity; 0 (not expressed), 1 (expressed). Edges indicate that genes act on each other, by means of rules represented by Boolean functions. The state of the network at each time step comprises the states of the individual nodes. PBNs extend Kauffman's Boolean Networks (BNs) [2]} by associating more than one function with each node, one of which executes at each time step with a certain probability. This stochasticity in the network model accounts for uncertainty in gene interaction, which is inherent not only in data collection but also in cell function. Both PBNs and BNs have been extensively used to model well-known regulatory networks, see seminal work in [3]}.
The dynamics of PBNs adhere to Markov chain theory [4]}
and dictate that the network, bluefrom any initial state, will eventually settle down to one of a limited set of steady states, the so-called attractors (fixed points or cyclic), that it cannot leave without intervention [5]}.
